Overview

Dataset statistics

Number of variables29
Number of observations2240
Missing cells24
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory507.6 KiB
Average record size in memory232.1 B

Variable types

Numeric15
Categorical14

Warnings

Z_CostContact has constant value "3" Constant
Z_Revenue has constant value "11" Constant
Dt_Customer has a high cardinality: 663 distinct values High cardinality
Income is highly correlated with MntWines and 4 other fieldsHigh correlation
Kidhome is highly correlated with NumCatalogPurchasesHigh correlation
MntWines is highly correlated with Income and 4 other fieldsHigh correlation
MntFruits is highly correlated with MntMeatProducts and 2 other fieldsHigh correlation
MntMeatProducts is highly correlated with Income and 6 other fieldsHigh correlation
MntFishProducts is highly correlated with MntFruits and 3 other fieldsHigh correlation
MntSweetProducts is highly correlated with MntFruits and 2 other fieldsHigh correlation
NumWebPurchases is highly correlated with MntWines and 1 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Income and 6 other fieldsHigh correlation
NumStorePurchases is highly correlated with Income and 3 other fieldsHigh correlation
NumWebVisitsMonth is highly correlated with Income and 2 other fieldsHigh correlation
Income is highly correlated with Kidhome and 10 other fieldsHigh correlation
Kidhome is highly correlated with Income and 4 other fieldsHigh correlation
MntWines is highly correlated with Income and 9 other fieldsHigh correlation
MntFruits is highly correlated with Income and 7 other fieldsHigh correlation
MntMeatProducts is highly correlated with Income and 9 other fieldsHigh correlation
MntFishProducts is highly correlated with Income and 7 other fieldsHigh correlation
MntSweetProducts is highly correlated with Income and 7 other fieldsHigh correlation
MntGoldProds is highly correlated with Income and 8 other fieldsHigh correlation
NumWebPurchases is highly correlated with Income and 5 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Income and 10 other fieldsHigh correlation
NumStorePurchases is highly correlated with Income and 9 other fieldsHigh correlation
NumWebVisitsMonth is highly correlated with Income and 1 other fieldsHigh correlation
Income is highly correlated with MntWines and 3 other fieldsHigh correlation
Kidhome is highly correlated with NumCatalogPurchasesHigh correlation
MntWines is highly correlated with Income and 4 other fieldsHigh correlation
MntFruits is highly correlated with MntMeatProducts and 2 other fieldsHigh correlation
MntMeatProducts is highly correlated with Income and 7 other fieldsHigh correlation
MntFishProducts is highly correlated with MntFruits and 3 other fieldsHigh correlation
MntSweetProducts is highly correlated with MntFruits and 2 other fieldsHigh correlation
NumWebPurchases is highly correlated with MntWines and 2 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Income and 5 other fieldsHigh correlation
NumStorePurchases is highly correlated with Income and 4 other fieldsHigh correlation
Income is highly correlated with NumWebVisitsMonth and 8 other fieldsHigh correlation
NumWebVisitsMonth is highly correlated with Income and 3 other fieldsHigh correlation
NumStorePurchases is highly correlated with Income and 10 other fieldsHigh correlation
AcceptedCmp4 is highly correlated with MntWinesHigh correlation
MntMeatProducts is highly correlated with Income and 3 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Income and 5 other fieldsHigh correlation
Kidhome is highly correlated with NumStorePurchases and 3 other fieldsHigh correlation
MntWines is highly correlated with Income and 9 other fieldsHigh correlation
MntFruits is highly correlated with Income and 4 other fieldsHigh correlation
Teenhome is highly correlated with NumDealsPurchasesHigh correlation
NumWebPurchases is highly correlated with NumStorePurchases and 3 other fieldsHigh correlation
MntFishProducts is highly correlated with Income and 6 other fieldsHigh correlation
AcceptedCmp1 is highly correlated with Income and 1 other fieldsHigh correlation
AcceptedCmp5 is highly correlated with Income and 2 other fieldsHigh correlation
NumDealsPurchases is highly correlated with NumWebVisitsMonth and 2 other fieldsHigh correlation
MntGoldProds is highly correlated with NumWebVisitsMonth and 4 other fieldsHigh correlation
MntSweetProducts is highly correlated with Kidhome and 4 other fieldsHigh correlation
AcceptedCmp1 is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
Response is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
AcceptedCmp4 is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
AcceptedCmp5 is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
AcceptedCmp2 is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
Complain is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
Z_Revenue is highly correlated with AcceptedCmp1 and 11 other fieldsHigh correlation
AcceptedCmp3 is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
Z_CostContact is highly correlated with AcceptedCmp1 and 11 other fieldsHigh correlation
Education is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
Kidhome is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
Marital_Status is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
Teenhome is highly correlated with Z_Revenue and 1 other fieldsHigh correlation
Income has 24 (1.1%) missing values Missing
ID has unique values Unique
Recency has 28 (1.2%) zeros Zeros
MntFruits has 400 (17.9%) zeros Zeros
MntFishProducts has 384 (17.1%) zeros Zeros
MntSweetProducts has 419 (18.7%) zeros Zeros
MntGoldProds has 61 (2.7%) zeros Zeros
NumDealsPurchases has 46 (2.1%) zeros Zeros
NumWebPurchases has 49 (2.2%) zeros Zeros
NumCatalogPurchases has 586 (26.2%) zeros Zeros

Reproduction

Analysis started2021-12-05 16:49:21.687371
Analysis finished2021-12-05 16:50:24.503957
Duration1 minute and 2.82 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

ID
Real number (ℝ≥0)

UNIQUE

Distinct2240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5592.159821
Minimum0
Maximum11191
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:26.338194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile576.85
Q12828.25
median5458.5
Q38427.75
95-th percentile10675.05
Maximum11191
Range11191
Interquartile range (IQR)5599.5

Descriptive statistics

Standard deviation3246.662198
Coefficient of variation (CV)0.5805739287
Kurtosis-1.190028038
Mean5592.159821
Median Absolute Deviation (MAD)2791
Skewness0.0398318728
Sum12526438
Variance10540815.43
MonotonicityNot monotonic
2021-12-05T12:50:26.500236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55241
 
< 0.1%
68851
 
< 0.1%
34781
 
< 0.1%
74941
 
< 0.1%
17631
 
< 0.1%
72501
 
< 0.1%
20051
 
< 0.1%
107701
 
< 0.1%
20721
 
< 0.1%
97431
 
< 0.1%
Other values (2230)2230
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
91
< 0.1%
131
< 0.1%
171
< 0.1%
201
< 0.1%
221
< 0.1%
241
< 0.1%
251
< 0.1%
351
< 0.1%
ValueCountFrequency (%)
111911
< 0.1%
111881
< 0.1%
111871
< 0.1%
111811
< 0.1%
111781
< 0.1%
111761
< 0.1%
111711
< 0.1%
111661
< 0.1%
111481
< 0.1%
111331
< 0.1%

Year_Birth
Real number (ℝ≥0)

Distinct59
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.805804
Minimum1893
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:26.773202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1893
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.98406946
Coefficient of variation (CV)0.006086973858
Kurtosis0.7174644425
Mean1968.805804
Median Absolute Deviation (MAD)9
Skewness-0.3499438592
Sum4410125
Variance143.6179207
MonotonicityNot monotonic
2021-12-05T12:50:26.946947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197689
 
4.0%
197187
 
3.9%
197583
 
3.7%
197279
 
3.5%
197877
 
3.4%
197077
 
3.4%
197374
 
3.3%
196574
 
3.3%
196971
 
3.2%
197469
 
3.1%
Other values (49)1460
65.2%
ValueCountFrequency (%)
18931
 
< 0.1%
18991
 
< 0.1%
19001
 
< 0.1%
19401
 
< 0.1%
19411
 
< 0.1%
19437
0.3%
19447
0.3%
19458
0.4%
194616
0.7%
194716
0.7%
ValueCountFrequency (%)
19962
 
0.1%
19955
 
0.2%
19943
 
0.1%
19935
 
0.2%
199213
0.6%
199115
0.7%
199018
0.8%
198930
1.3%
198829
1.3%
198727
1.2%

Education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Graduation
1127 
PhD
486 
Master
370 
2n Cycle
203 
Basic
 
54

Length

Max length10
Median length10
Mean length7.51875
Min length3

Characters and Unicode

Total characters16842
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowPhD

Common Values

ValueCountFrequency (%)
Graduation1127
50.3%
PhD486
21.7%
Master370
 
16.5%
2n Cycle203
 
9.1%
Basic54
 
2.4%

Length

2021-12-05T12:50:27.249651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:27.470981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
graduation1127
46.1%
phd486
19.9%
master370
 
15.1%
2n203
 
8.3%
cycle203
 
8.3%
basic54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a2678
15.9%
r1497
8.9%
t1497
8.9%
n1330
 
7.9%
i1181
 
7.0%
G1127
 
6.7%
d1127
 
6.7%
u1127
 
6.7%
o1127
 
6.7%
e573
 
3.4%
Other values (12)3578
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13710
81.4%
Uppercase Letter2726
 
16.2%
Decimal Number203
 
1.2%
Space Separator203
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2678
19.5%
r1497
10.9%
t1497
10.9%
n1330
9.7%
i1181
8.6%
d1127
8.2%
u1127
8.2%
o1127
8.2%
e573
 
4.2%
h486
 
3.5%
Other values (4)1087
7.9%
Uppercase Letter
ValueCountFrequency (%)
G1127
41.3%
P486
17.8%
D486
17.8%
M370
 
13.6%
C203
 
7.4%
B54
 
2.0%
Decimal Number
ValueCountFrequency (%)
2203
100.0%
Space Separator
ValueCountFrequency (%)
203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16436
97.6%
Common406
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2678
16.3%
r1497
9.1%
t1497
9.1%
n1330
8.1%
i1181
 
7.2%
G1127
 
6.9%
d1127
 
6.9%
u1127
 
6.9%
o1127
 
6.9%
e573
 
3.5%
Other values (10)3172
19.3%
Common
ValueCountFrequency (%)
2203
50.0%
203
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2678
15.9%
r1497
8.9%
t1497
8.9%
n1330
 
7.9%
i1181
 
7.0%
G1127
 
6.7%
d1127
 
6.7%
u1127
 
6.7%
o1127
 
6.7%
e573
 
3.4%
Other values (12)3578
21.2%

Marital_Status
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Married
864 
Together
580 
Single
480 
Divorced
232 
Widow
 
77
Other values (3)
 
7

Length

Max length8
Median length7
Mean length7.073214286
Min length4

Characters and Unicode

Total characters15844
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowTogether
4th rowTogether
5th rowMarried

Common Values

ValueCountFrequency (%)
Married864
38.6%
Together580
25.9%
Single480
21.4%
Divorced232
 
10.4%
Widow77
 
3.4%
Alone3
 
0.1%
Absurd2
 
0.1%
YOLO2
 
0.1%

Length

2021-12-05T12:50:27.726232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:27.832522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
married864
38.6%
together580
25.9%
single480
21.4%
divorced232
 
10.4%
widow77
 
3.4%
alone3
 
0.1%
absurd2
 
0.1%
yolo2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e2739
17.3%
r2542
16.0%
i1653
10.4%
d1175
7.4%
g1060
 
6.7%
o892
 
5.6%
M864
 
5.5%
a864
 
5.5%
T580
 
3.7%
t580
 
3.7%
Other values (16)2895
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13598
85.8%
Uppercase Letter2246
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2739
20.1%
r2542
18.7%
i1653
12.2%
d1175
8.6%
g1060
 
7.8%
o892
 
6.6%
a864
 
6.4%
t580
 
4.3%
h580
 
4.3%
n483
 
3.6%
Other values (7)1030
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
M864
38.5%
T580
25.8%
S480
21.4%
D232
 
10.3%
W77
 
3.4%
A5
 
0.2%
O4
 
0.2%
Y2
 
0.1%
L2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin15844
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2739
17.3%
r2542
16.0%
i1653
10.4%
d1175
7.4%
g1060
 
6.7%
o892
 
5.6%
M864
 
5.5%
a864
 
5.5%
T580
 
3.7%
t580
 
3.7%
Other values (16)2895
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2739
17.3%
r2542
16.0%
i1653
10.4%
d1175
7.4%
g1060
 
6.7%
o892
 
5.6%
M864
 
5.5%
a864
 
5.5%
T580
 
3.7%
t580
 
3.7%
Other values (16)2895
18.3%

Income
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1974
Distinct (%)89.1%
Missing24
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean52247.25135
Minimum1730
Maximum666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:27.980990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile18985.5
Q135303
median51381.5
Q368522
95-th percentile84130
Maximum666666
Range664936
Interquartile range (IQR)33219

Descriptive statistics

Standard deviation25173.07666
Coefficient of variation (CV)0.4818067173
Kurtosis159.6366996
Mean52247.25135
Median Absolute Deviation (MAD)16557.5
Skewness6.763487373
Sum115779909
Variance633683788.6
MonotonicityNot monotonic
2021-12-05T12:50:28.163168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
750012
 
0.5%
358604
 
0.2%
377603
 
0.1%
838443
 
0.1%
638413
 
0.1%
189293
 
0.1%
470253
 
0.1%
341763
 
0.1%
484323
 
0.1%
801343
 
0.1%
Other values (1964)2176
97.1%
(Missing)24
 
1.1%
ValueCountFrequency (%)
17301
< 0.1%
24471
< 0.1%
35021
< 0.1%
40231
< 0.1%
44281
< 0.1%
48611
< 0.1%
53051
< 0.1%
56481
< 0.1%
65601
< 0.1%
68351
< 0.1%
ValueCountFrequency (%)
6666661
< 0.1%
1623971
< 0.1%
1608031
< 0.1%
1577331
< 0.1%
1572431
< 0.1%
1571461
< 0.1%
1569241
< 0.1%
1539241
< 0.1%
1137341
< 0.1%
1054711
< 0.1%

Kidhome
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1293 
1
899 
2
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Length

2021-12-05T12:50:28.468573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:28.547605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Most occurring characters

ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Teenhome
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1158 
1
1030 
2
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Length

2021-12-05T12:50:28.766603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:28.847633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Most occurring characters

ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Dt_Customer
Categorical

HIGH CARDINALITY

Distinct663
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
31-08-2012
 
12
12-09-2012
 
11
14-02-2013
 
11
12-05-2014
 
11
20-08-2013
 
10
Other values (658)
2185 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters22400
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)4.8%

Sample

1st row04-09-2012
2nd row08-03-2014
3rd row21-08-2013
4th row10-02-2014
5th row19-01-2014

Common Values

ValueCountFrequency (%)
31-08-201212
 
0.5%
12-09-201211
 
0.5%
14-02-201311
 
0.5%
12-05-201411
 
0.5%
20-08-201310
 
0.4%
22-05-201410
 
0.4%
05-04-20149
 
0.4%
23-03-20149
 
0.4%
02-01-20139
 
0.4%
01-03-20149
 
0.4%
Other values (653)2139
95.5%

Length

2021-12-05T12:50:29.250792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
31-08-201212
 
0.5%
12-09-201211
 
0.5%
14-02-201311
 
0.5%
12-05-201411
 
0.5%
20-08-201310
 
0.4%
22-05-201410
 
0.4%
05-04-20149
 
0.4%
23-03-20149
 
0.4%
02-01-20139
 
0.4%
01-03-20149
 
0.4%
Other values (653)2139
95.5%

Most occurring characters

ValueCountFrequency (%)
04980
22.2%
-4480
20.0%
14228
18.9%
24076
18.2%
31742
 
7.8%
4928
 
4.1%
8445
 
2.0%
5424
 
1.9%
9403
 
1.8%
6363
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17920
80.0%
Dash Punctuation4480
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04980
27.8%
14228
23.6%
24076
22.7%
31742
 
9.7%
4928
 
5.2%
8445
 
2.5%
5424
 
2.4%
9403
 
2.2%
6363
 
2.0%
7331
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
-4480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common22400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04980
22.2%
-4480
20.0%
14228
18.9%
24076
18.2%
31742
 
7.8%
4928
 
4.1%
8445
 
2.0%
5424
 
1.9%
9403
 
1.8%
6363
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII22400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04980
22.2%
-4480
20.0%
14228
18.9%
24076
18.2%
31742
 
7.8%
4928
 
4.1%
8445
 
2.0%
5424
 
1.9%
9403
 
1.8%
6363
 
1.6%

Recency
Real number (ℝ≥0)

ZEROS

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.109375
Minimum0
Maximum99
Zeros28
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:29.401861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.96245281
Coefficient of variation (CV)0.5897540502
Kurtosis-1.201896799
Mean49.109375
Median Absolute Deviation (MAD)25
Skewness-0.001986658634
Sum110005
Variance838.8236727
MonotonicityNot monotonic
2021-12-05T12:50:29.583903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5637
 
1.7%
3032
 
1.4%
5432
 
1.4%
4631
 
1.4%
9230
 
1.3%
4930
 
1.3%
6530
 
1.3%
329
 
1.3%
2929
 
1.3%
7129
 
1.3%
Other values (90)1931
86.2%
ValueCountFrequency (%)
028
1.2%
124
1.1%
228
1.2%
329
1.3%
427
1.2%
515
0.7%
621
0.9%
712
0.5%
825
1.1%
924
1.1%
ValueCountFrequency (%)
9917
0.8%
9822
1.0%
9720
0.9%
9625
1.1%
9519
0.8%
9426
1.2%
9321
0.9%
9230
1.3%
9118
0.8%
9020
0.9%

MntWines
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct776
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.9357143
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:29.756841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123.75
median173.5
Q3504.25
95-th percentile1000
Maximum1493
Range1493
Interquartile range (IQR)480.5

Descriptive statistics

Standard deviation336.5973926
Coefficient of variation (CV)1.107462456
Kurtosis0.5987435935
Mean303.9357143
Median Absolute Deviation (MAD)164.5
Skewness1.175770564
Sum680816
Variance113297.8047
MonotonicityNot monotonic
2021-12-05T12:50:29.935822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242
 
1.9%
540
 
1.8%
137
 
1.7%
637
 
1.7%
433
 
1.5%
830
 
1.3%
330
 
1.3%
928
 
1.2%
1225
 
1.1%
1024
 
1.1%
Other values (766)1914
85.4%
ValueCountFrequency (%)
013
 
0.6%
137
1.7%
242
1.9%
330
1.3%
433
1.5%
540
1.8%
637
1.7%
722
1.0%
830
1.3%
928
1.2%
ValueCountFrequency (%)
14931
< 0.1%
14922
0.1%
14861
< 0.1%
14782
0.1%
14621
< 0.1%
14591
< 0.1%
14491
< 0.1%
13961
< 0.1%
13941
< 0.1%
13791
< 0.1%

MntFruits
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.30223214
Minimum0
Maximum199
Zeros400
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:30.115738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile123
Maximum199
Range199
Interquartile range (IQR)32

Descriptive statistics

Standard deviation39.77343376
Coefficient of variation (CV)1.51216952
Kurtosis4.050976251
Mean26.30223214
Median Absolute Deviation (MAD)8
Skewness2.102063305
Sum58917
Variance1581.926033
MonotonicityNot monotonic
2021-12-05T12:50:30.312076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0400
 
17.9%
1162
 
7.2%
2120
 
5.4%
3116
 
5.2%
4104
 
4.6%
767
 
3.0%
565
 
2.9%
662
 
2.8%
1250
 
2.2%
848
 
2.1%
Other values (148)1046
46.7%
ValueCountFrequency (%)
0400
17.9%
1162
7.2%
2120
 
5.4%
3116
 
5.2%
4104
 
4.6%
565
 
2.9%
662
 
2.8%
767
 
3.0%
848
 
2.1%
935
 
1.6%
ValueCountFrequency (%)
1992
0.1%
1971
 
< 0.1%
1943
0.1%
1932
0.1%
1901
 
< 0.1%
1891
 
< 0.1%
1852
0.1%
1841
 
< 0.1%
1833
0.1%
1811
 
< 0.1%

MntMeatProducts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct558
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.95
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:30.493642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median67
Q3232
95-th percentile687.1
Maximum1725
Range1725
Interquartile range (IQR)216

Descriptive statistics

Standard deviation225.7153725
Coefficient of variation (CV)1.351993846
Kurtosis5.516724101
Mean166.95
Median Absolute Deviation (MAD)59
Skewness2.083233113
Sum373968
Variance50947.42939
MonotonicityNot monotonic
2021-12-05T12:50:30.654902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
753
 
2.4%
550
 
2.2%
1149
 
2.2%
846
 
2.1%
643
 
1.9%
1040
 
1.8%
340
 
1.8%
938
 
1.7%
1636
 
1.6%
1235
 
1.6%
Other values (548)1810
80.8%
ValueCountFrequency (%)
01
 
< 0.1%
114
 
0.6%
230
1.3%
340
1.8%
430
1.3%
550
2.2%
643
1.9%
753
2.4%
846
2.1%
938
1.7%
ValueCountFrequency (%)
17252
0.1%
16221
< 0.1%
16071
< 0.1%
15821
< 0.1%
9841
< 0.1%
9811
< 0.1%
9741
< 0.1%
9681
< 0.1%
9611
< 0.1%
9512
0.1%

MntFishProducts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct182
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.52544643
Minimum0
Maximum259
Zeros384
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:30.814906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile168.05
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.6289794
Coefficient of variation (CV)1.45578493
Kurtosis3.096460912
Mean37.52544643
Median Absolute Deviation (MAD)12
Skewness1.919768971
Sum84057
Variance2984.325391
MonotonicityNot monotonic
2021-12-05T12:50:31.009162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0384
 
17.1%
2156
 
7.0%
3130
 
5.8%
4108
 
4.8%
682
 
3.7%
766
 
2.9%
858
 
2.6%
1055
 
2.5%
1348
 
2.1%
1247
 
2.1%
Other values (172)1106
49.4%
ValueCountFrequency (%)
0384
17.1%
110
 
0.4%
2156
7.0%
3130
 
5.8%
4108
 
4.8%
51
 
< 0.1%
682
 
3.7%
766
 
2.9%
858
 
2.6%
1055
 
2.5%
ValueCountFrequency (%)
2591
 
< 0.1%
2583
0.1%
2541
 
< 0.1%
2531
 
< 0.1%
2503
0.1%
2471
 
< 0.1%
2461
 
< 0.1%
2421
 
< 0.1%
2402
0.1%
2372
0.1%

MntSweetProducts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct177
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.06294643
Minimum0
Maximum263
Zeros419
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:31.172165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile126
Maximum263
Range263
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.28049849
Coefficient of variation (CV)1.525351225
Kurtosis4.376548261
Mean27.06294643
Median Absolute Deviation (MAD)8
Skewness2.136080712
Sum60621
Variance1704.079555
MonotonicityNot monotonic
2021-12-05T12:50:31.343158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0419
 
18.7%
1161
 
7.2%
2128
 
5.7%
3101
 
4.5%
482
 
3.7%
565
 
2.9%
664
 
2.9%
757
 
2.5%
856
 
2.5%
1245
 
2.0%
Other values (167)1062
47.4%
ValueCountFrequency (%)
0419
18.7%
1161
 
7.2%
2128
 
5.7%
3101
 
4.5%
482
 
3.7%
565
 
2.9%
664
 
2.9%
757
 
2.5%
856
 
2.5%
942
 
1.9%
ValueCountFrequency (%)
2631
 
< 0.1%
2621
 
< 0.1%
1981
 
< 0.1%
1971
 
< 0.1%
1961
 
< 0.1%
1951
 
< 0.1%
1943
0.1%
1923
0.1%
1911
 
< 0.1%
1892
0.1%

MntGoldProds
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct213
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.021875
Minimum0
Maximum362
Zeros61
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:31.615438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q356
95-th percentile165.05
Maximum362
Range362
Interquartile range (IQR)47

Descriptive statistics

Standard deviation52.16743891
Coefficient of variation (CV)1.185034461
Kurtosis3.55170925
Mean44.021875
Median Absolute Deviation (MAD)18
Skewness1.886105609
Sum98609
Variance2721.441683
MonotonicityNot monotonic
2021-12-05T12:50:31.768458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173
 
3.3%
470
 
3.1%
369
 
3.1%
563
 
2.8%
1263
 
2.8%
262
 
2.8%
061
 
2.7%
657
 
2.5%
754
 
2.4%
1049
 
2.2%
Other values (203)1619
72.3%
ValueCountFrequency (%)
061
2.7%
173
3.3%
262
2.8%
369
3.1%
470
3.1%
563
2.8%
657
2.5%
754
2.4%
840
1.8%
944
2.0%
ValueCountFrequency (%)
3621
< 0.1%
3211
< 0.1%
2911
< 0.1%
2621
< 0.1%
2491
< 0.1%
2481
< 0.1%
2471
< 0.1%
2461
< 0.1%
2451
< 0.1%
2422
0.1%

NumDealsPurchases
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.325
Minimum0
Maximum15
Zeros46
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:31.906590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.932237501
Coefficient of variation (CV)0.8310698928
Kurtosis8.936914321
Mean2.325
Median Absolute Deviation (MAD)1
Skewness2.418569388
Sum5208
Variance3.73354176
MonotonicityNot monotonic
2021-12-05T12:50:32.026622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1970
43.3%
2497
22.2%
3297
 
13.3%
4189
 
8.4%
594
 
4.2%
661
 
2.7%
046
 
2.1%
740
 
1.8%
814
 
0.6%
98
 
0.4%
Other values (5)24
 
1.1%
ValueCountFrequency (%)
046
 
2.1%
1970
43.3%
2497
22.2%
3297
 
13.3%
4189
 
8.4%
594
 
4.2%
661
 
2.7%
740
 
1.8%
814
 
0.6%
98
 
0.4%
ValueCountFrequency (%)
157
 
0.3%
133
 
0.1%
124
 
0.2%
115
 
0.2%
105
 
0.2%
98
 
0.4%
814
 
0.6%
740
1.8%
661
2.7%
594
4.2%

NumWebPurchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.084821429
Minimum0
Maximum27
Zeros49
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:32.156018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.778714147
Coefficient of variation (CV)0.680253518
Kurtosis5.703128364
Mean4.084821429
Median Absolute Deviation (MAD)2
Skewness1.382794296
Sum9150
Variance7.721252313
MonotonicityNot monotonic
2021-12-05T12:50:32.287574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2373
16.7%
1354
15.8%
3336
15.0%
4280
12.5%
5220
9.8%
6205
9.2%
7155
6.9%
8102
 
4.6%
975
 
3.3%
049
 
2.2%
Other values (5)91
 
4.1%
ValueCountFrequency (%)
049
 
2.2%
1354
15.8%
2373
16.7%
3336
15.0%
4280
12.5%
5220
9.8%
6205
9.2%
7155
6.9%
8102
 
4.6%
975
 
3.3%
ValueCountFrequency (%)
272
 
0.1%
251
 
< 0.1%
231
 
< 0.1%
1144
 
2.0%
1043
 
1.9%
975
 
3.3%
8102
4.6%
7155
6.9%
6205
9.2%
5220
9.8%

NumCatalogPurchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.662053571
Minimum0
Maximum28
Zeros586
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:32.404611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.923100656
Coefficient of variation (CV)1.098062296
Kurtosis8.047436789
Mean2.662053571
Median Absolute Deviation (MAD)2
Skewness1.880988778
Sum5963
Variance8.544517442
MonotonicityNot monotonic
2021-12-05T12:50:32.505642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0586
26.2%
1497
22.2%
2276
12.3%
3184
 
8.2%
4182
 
8.1%
5140
 
6.2%
6128
 
5.7%
779
 
3.5%
855
 
2.5%
1048
 
2.1%
Other values (4)65
 
2.9%
ValueCountFrequency (%)
0586
26.2%
1497
22.2%
2276
12.3%
3184
 
8.2%
4182
 
8.1%
5140
 
6.2%
6128
 
5.7%
779
 
3.5%
855
 
2.5%
942
 
1.9%
ValueCountFrequency (%)
283
 
0.1%
221
 
< 0.1%
1119
 
0.8%
1048
 
2.1%
942
 
1.9%
855
 
2.5%
779
3.5%
6128
5.7%
5140
6.2%
4182
8.1%

NumStorePurchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.790178571
Minimum0
Maximum13
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:32.624645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.250958146
Coefficient of variation (CV)0.5614607746
Kurtosis-0.6220482771
Mean5.790178571
Median Absolute Deviation (MAD)2
Skewness0.7022372855
Sum12970
Variance10.56872886
MonotonicityNot monotonic
2021-12-05T12:50:32.766051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3490
21.9%
4323
14.4%
2223
10.0%
5212
9.5%
6178
 
7.9%
8149
 
6.7%
7143
 
6.4%
10125
 
5.6%
9106
 
4.7%
12105
 
4.7%
Other values (4)186
 
8.3%
ValueCountFrequency (%)
015
 
0.7%
17
 
0.3%
2223
10.0%
3490
21.9%
4323
14.4%
5212
9.5%
6178
 
7.9%
7143
 
6.4%
8149
 
6.7%
9106
 
4.7%
ValueCountFrequency (%)
1383
 
3.7%
12105
 
4.7%
1181
 
3.6%
10125
 
5.6%
9106
 
4.7%
8149
6.7%
7143
6.4%
6178
7.9%
5212
9.5%
4323
14.4%

NumWebVisitsMonth
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.316517857
Minimum0
Maximum20
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-12-05T12:50:32.899998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.42664501
Coefficient of variation (CV)0.4564350341
Kurtosis1.821613827
Mean5.316517857
Median Absolute Deviation (MAD)2
Skewness0.2079255568
Sum11909
Variance5.888606002
MonotonicityNot monotonic
2021-12-05T12:50:33.026058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7393
17.5%
8342
15.3%
6340
15.2%
5281
12.5%
4218
9.7%
3205
9.2%
2202
9.0%
1153
 
6.8%
983
 
3.7%
011
 
0.5%
Other values (6)12
 
0.5%
ValueCountFrequency (%)
011
 
0.5%
1153
 
6.8%
2202
9.0%
3205
9.2%
4218
9.7%
5281
12.5%
6340
15.2%
7393
17.5%
8342
15.3%
983
 
3.7%
ValueCountFrequency (%)
203
 
0.1%
192
 
0.1%
171
 
< 0.1%
142
 
0.1%
131
 
< 0.1%
103
 
0.1%
983
 
3.7%
8342
15.3%
7393
17.5%
6340
15.2%

AcceptedCmp3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Length

2021-12-05T12:50:33.285511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:33.362572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

AcceptedCmp4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2073 
1
 
167

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Length

2021-12-05T12:50:33.567173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:33.638209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Most occurring characters

ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

AcceptedCmp5
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Length

2021-12-05T12:50:33.862233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:34.058293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

AcceptedCmp1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2096 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Length

2021-12-05T12:50:34.276305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:34.349303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Most occurring characters

ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

AcceptedCmp2
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2210 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Length

2021-12-05T12:50:34.538440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:34.612356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Most occurring characters

ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Complain
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2219 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Length

2021-12-05T12:50:34.808493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:34.880038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Most occurring characters

ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Z_CostContact
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
3
2240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
32240
100.0%

Length

2021-12-05T12:50:35.136106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:35.205107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
32240
100.0%

Most occurring characters

ValueCountFrequency (%)
32240
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
32240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
32240
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32240
100.0%

Z_Revenue
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
11
2240 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4480
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
112240
100.0%

Length

2021-12-05T12:50:35.411453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:35.503704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
112240
100.0%

Most occurring characters

ValueCountFrequency (%)
14480
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4480
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14480
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14480
100.0%

Response
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1906 
1
334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Length

2021-12-05T12:50:35.744718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-05T12:50:35.830465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Most occurring characters

ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Interactions

2021-12-05T12:49:41.514446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:46.712332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:46.976640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:47.124120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:47.264246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:47.426637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:47.564450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:47.703826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:47.833832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:47.966128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:48.104764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:48.245974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:48.386561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:48.528726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:48.656949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:48.791985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:48.920997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:49.071004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:49.208651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:49.346359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:49.485557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:49.730064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:49.862062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:49.991811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:50.130865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:50.272237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:50.431239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:50.569285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:50.710021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:50.882359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:51.034647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:51.189430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:51.327611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:51.476612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:51.616675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:51.772674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:51.920675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:52.060707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:52.203674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:52.339481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:52.477028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:52.616050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:52.771053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:53.005322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-05T12:49:53.137357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-05T12:50:37.457717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-05T12:50:37.841791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-05T12:50:38.219969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-12-05T12:50:38.558482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-12-05T12:50:20.018975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-05T12:50:21.342546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-05T12:50:23.702042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainZ_CostContactZ_RevenueResponse
055241957GraduationSingle58138.00004-09-2012586358854617288883810470000003111
121741954GraduationSingle46344.01108-03-2014381116216211250000003110
241411965GraduationTogether71613.00021-08-2013264264912711121421821040000003110
361821984GraduationTogether26646.01010-02-201426114201035220460000003110
453241981PhDMarried58293.01019-01-20149417343118462715553650000003110
574461967MasterTogether62513.00109-09-2013165204298042142641060000003110
69651971GraduationDivorced55635.00113-11-20123423565164504927473760000003110
761771985PhDMarried33454.01008-05-2013327610563123240480000003110
848551974PhDTogether30351.01006-06-20131914024332130290000003111
958991950PhDTogether5648.01113-03-201468280611131100201000003110

Last rows

IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainZ_CostContactZ_RevenueResponse
223070041984GraduationSingle11012.01016-03-201382243267123331291000003110
223198171970MasterSingle44802.00021-08-201271853101431310202941280000003110
223280801986GraduationSingle26816.00017-08-201250516343100340000003110
223394321977GraduationTogether666666.01002-06-201323914188112431360000003110
223483721974GraduationMarried34421.01001-07-201381337629110270000003110
2235108701967GraduationMarried61223.00113-06-2013467094318242118247293450000003110
223640011946PhDTogether64014.02110-06-201456406030008782570001003110
223772701981GraduationDivorced56981.00025-01-201491908482173212241231360100003110
223882351956MasterTogether69245.00124-01-20148428302148030612651030000003110
223994051954PhDMarried52869.01115-10-201240843612121331470000003111